




import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

   if is_vision_available():
      from transformers import MaskaFormerImageProcessor

if is_vision_available():
   from PIL import Image





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						List[Any]							,							_lowerCAmelCase    :						Optional[int]							,							_lowerCAmelCase    :						str=2							,							_lowerCAmelCase    :						str=True							,							_lowerCAmelCase    :						Optional[Any]=False							,							_lowerCAmelCase    :						Tuple=10							,							_lowerCAmelCase    :						List[str]=3							,							_lowerCAmelCase    :						Union[str, Any]=32 * 8							,							_lowerCAmelCase    :						Dict=32 * 8							,							_lowerCAmelCase    :						Optional[Any]=4							,							_lowerCAmelCase    :						List[str]=64							,							):
       SCREAMING_SNAKE_CASE_      		=     parent
       SCREAMING_SNAKE_CASE_      		=     batch_size
       SCREAMING_SNAKE_CASE_      		=     is_training
       SCREAMING_SNAKE_CASE_      		=     use_auxiliary_loss
       SCREAMING_SNAKE_CASE_      		=     num_queries
       SCREAMING_SNAKE_CASE_      		=     num_channels
       SCREAMING_SNAKE_CASE_      		=     min_size
       SCREAMING_SNAKE_CASE_      		=     max_size
       SCREAMING_SNAKE_CASE_      		=     num_labels
       SCREAMING_SNAKE_CASE_      		=     hidden_dim
       SCREAMING_SNAKE_CASE_      		=     hidden_dim



 def  lowerCAmelCase_    (				self    :						List[Any]		):
       SCREAMING_SNAKE_CASE_      		=     floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]		).to(
           _lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     torch.ones([self.batch_size, self.min_size, self.max_size]							,							device=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     (
           torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]							,							device=_lowerCAmelCase		) > 0.5
       ).float()
       SCREAMING_SNAKE_CASE_      		=     (torch.rand((self.batch_size, self.num_labels)							,							device=_lowerCAmelCase		) > 0.5).long()

       SCREAMING_SNAKE_CASE_      		=     self.get_config()
       return config, pixel_values, pixel_mask, mask_labels, class_labels



 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_      		=     MaskaFormerConfig(
           hidden_size=self.hidden_dim							,							)
       SCREAMING_SNAKE_CASE_      		=     self.num_queries
       SCREAMING_SNAKE_CASE_      		=     self.num_labels

       SCREAMING_SNAKE_CASE_      		=     [1, 1, 1, 1]
       SCREAMING_SNAKE_CASE_      		=     self.num_channels

       SCREAMING_SNAKE_CASE_      		=     64
       SCREAMING_SNAKE_CASE_      		=     128
       SCREAMING_SNAKE_CASE_      		=     self.hidden_dim
       SCREAMING_SNAKE_CASE_      		=     self.hidden_dim
       SCREAMING_SNAKE_CASE_      		=     self.hidden_dim
       return config



 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE_      		=     {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
       return config, inputs_dict



 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						List[str]		):
       SCREAMING_SNAKE_CASE_      		=     output.encoder_hidden_states
       SCREAMING_SNAKE_CASE_      		=     output.pixel_decoder_hidden_states
       SCREAMING_SNAKE_CASE_      		=     output.transformer_decoder_hidden_states

       self.parent.assertTrue(len(_lowerCAmelCase		)							,							len(config.backbone_config.depths		)		)
       self.parent.assertTrue(len(_lowerCAmelCase		)							,							len(config.backbone_config.depths		)		)
       self.parent.assertTrue(len(_lowerCAmelCase		)							,							config.decoder_layers		)



 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						Union[str, Any]							,							_lowerCAmelCase    :						List[str]							,							_lowerCAmelCase    :						Tuple=False		):
       with torch.no_grad():
             SCREAMING_SNAKE_CASE_      		=     MaskaFormerModel(config=_lowerCAmelCase		)
             model.to(_lowerCAmelCase		)
             model.eval()

             SCREAMING_SNAKE_CASE_      		=     model(pixel_values=_lowerCAmelCase							,							pixel_mask=_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							output_hidden_states=_lowerCAmelCase		)

       self.parent.assertEqual(
           output.transformer_decoder_last_hidden_state.shape							,							(self.batch_size, self.num_queries, self.hidden_dim)							,							)
       # let's ensure the other two hidden state exists
       self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None		)
       self.parent.assertTrue(output.encoder_last_hidden_state is not None		)

       if output_hidden_states:
             self.check_output_hidden_state(_lowerCAmelCase							,							_lowerCAmelCase		)




 def  lowerCAmelCase_    (				self    :						Union[str, Any]							,							_lowerCAmelCase    :						Dict							,							_lowerCAmelCase    :						Optional[Any]							,							_lowerCAmelCase    :						List[Any]							,							_lowerCAmelCase    :						int							,							_lowerCAmelCase    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     MaskaFormerForUniversalSegmentation(config=_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.eval()

       def comm_check_on_output(_lowerCAmelCase    :						Any		):
             # let's still check that all the required stuff is there
             self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None		)
             self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None		)
             self.parent.assertTrue(result.encoder_last_hidden_state is not None		)
             # okay, now we need to check the logits shape
             # due to the encoder compression, masks have a //4 spatial size
             self.parent.assertEqual(
                 result.masks_queries_logits.shape							,							(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)							,							)
             # + 1 for null class
             self.parent.assertEqual(
                 result.class_queries_logits.shape							,							(self.batch_size, self.num_queries, self.num_labels + 1)		)

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_      		=     model(pixel_values=_lowerCAmelCase							,							pixel_mask=_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase		)

             comm_check_on_output(_lowerCAmelCase		)

             SCREAMING_SNAKE_CASE_      		=     model(
                 pixel_values=_lowerCAmelCase							,							pixel_mask=_lowerCAmelCase							,							mask_labels=_lowerCAmelCase							,							class_labels=_lowerCAmelCase		)

       comm_check_on_output(_lowerCAmelCase		)

       self.parent.assertTrue(result.loss is not None		)
       self.parent.assertEqual(result.loss.shape							,							torch.Size([1]		)		)





@require_torch
class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  unittest.TestCase   ):


 '''simple docstring'''







 lowercase_							       =       (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
 lowercase_							       =       {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

 lowercase_							       =       False
 lowercase_							       =       False
 lowercase_							       =       False
 lowercase_							       =       False



 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       SCREAMING_SNAKE_CASE_      		=     MaskaFormerModelTester(self		)
       SCREAMING_SNAKE_CASE_      		=     ConfigTester(self							,							config_class=_lowerCAmelCase							,							has_text_modality=_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						str		):
       self.config_tester.run_common_tests()



 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskaformer_model(_lowerCAmelCase							,							**_lowerCAmelCase							,							output_hidden_states=_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_lowerCAmelCase		)



 @unittest.skip(reason='Mask2Former does not use inputs_embeds'		)
 def  lowerCAmelCase_    (				self    :						int		):
       pass



 @unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'		)
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       pass



 @unittest.skip(reason='Mask2Former is not a generative model'		)
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       pass



 @unittest.skip(reason='Mask2Former does not use token embeddings'		)
 def  lowerCAmelCase_    (				self    :						int		):
       pass



 @require_torch_multi_gpu
 @unittest.skip(
     reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'		)
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       pass



 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'		)
 def  lowerCAmelCase_    (				self    :						List[Any]		):
       pass



 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE_      		=     model_class(_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     inspect.signature(model.forward		)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             SCREAMING_SNAKE_CASE_      		=     [*signature.parameters.keys()]

             SCREAMING_SNAKE_CASE_      		=     ['pixel_values']
             self.assertListEqual(arg_names[:1]							,							_lowerCAmelCase		)



 @slow
 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
             SCREAMING_SNAKE_CASE_      		=     MaskaFormerModel.from_pretrained(_lowerCAmelCase		)
             self.assertIsNotNone(_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_      		=     (self.model_tester.min_size,) * 2
       SCREAMING_SNAKE_CASE_      		=     {
           'pixel_values': torch.randn((2, 3, *size)							,							device=_lowerCAmelCase		),
           'mask_labels': torch.randn((2, 10, *size)							,							device=_lowerCAmelCase		),
           'class_labels': torch.zeros(2							,							10							,							device=_lowerCAmelCase		).long(),
       }
       SCREAMING_SNAKE_CASE_      		=     self.model_tester.get_config()

       SCREAMING_SNAKE_CASE_      		=     MaskaFormerForUniversalSegmentation(_lowerCAmelCase		).to(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		)
       self.assertTrue(outputs.loss is not None		)



 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.create_and_check_maskaformer_model(_lowerCAmelCase							,							**_lowerCAmelCase							,							output_hidden_states=_lowerCAmelCase		)



 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE_      		=     model_class(_lowerCAmelCase		).to(_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase							,							output_attentions=_lowerCAmelCase		)
             self.assertTrue(outputs.attentions is not None		)



 def  lowerCAmelCase_    (				self    :						Tuple		):
       if not self.model_tester.is_training:
             return

       SCREAMING_SNAKE_CASE_      		=     self.all_model_classes[1]
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()

       SCREAMING_SNAKE_CASE_      		=     model_class(_lowerCAmelCase		)
       model.to(_lowerCAmelCase		)
       model.train()

       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							mask_labels=_lowerCAmelCase							,							class_labels=_lowerCAmelCase		).loss
       loss.backward()




 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     self.all_model_classes[1]
       SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.model_tester.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE_      		=     True
       SCREAMING_SNAKE_CASE_      		=     True

       SCREAMING_SNAKE_CASE_      		=     model_class(_lowerCAmelCase		).to(_lowerCAmelCase		)
       model.train()

       SCREAMING_SNAKE_CASE_      		=     model(_lowerCAmelCase							,							mask_labels=_lowerCAmelCase							,							class_labels=_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     outputs.encoder_hidden_states[0]
       encoder_hidden_states.retain_grad()

       SCREAMING_SNAKE_CASE_      		=     outputs.pixel_decoder_hidden_states[0]
       pixel_decoder_hidden_states.retain_grad()

       SCREAMING_SNAKE_CASE_      		=     outputs.transformer_decoder_hidden_states[0]
       transformer_decoder_hidden_states.retain_grad()

       SCREAMING_SNAKE_CASE_      		=     outputs.attentions[0]
       attentions.retain_grad()

       outputs.loss.backward(retain_graph=_lowerCAmelCase		)

       self.assertIsNotNone(encoder_hidden_states.grad		)
       self.assertIsNotNone(pixel_decoder_hidden_states.grad		)
       self.assertIsNotNone(transformer_decoder_hidden_states.grad		)
       self.assertIsNotNone(attentions.grad		)


lowerCamelCase__					:  List[Any]      							=				1E-4

def    UpperCAmelCase_				( )   ->					Union[str, Any]:
      SCREAMING_SNAKE_CASE_      		=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
      return image





@require_vision
@slow
class 			lowerCamelCase_				( unittest.TestCase   ):


 '''simple docstring'''







 @cached_property
 def  lowerCAmelCase_    (				self    :						Any		):
       return "facebook/mask2former-swin-small-coco-instance"



 @cached_property
 def  lowerCAmelCase_    (				self    :						int		):
       return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints		) if is_vision_available() else None



 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     MaskaFormerModel.from_pretrained(self.model_checkpoints		).to(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.default_image_processor
       SCREAMING_SNAKE_CASE_      		=     prepare_img()
       SCREAMING_SNAKE_CASE_      		=     image_processor(_lowerCAmelCase							,							return_tensors='pt'		).to(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     inputs['pixel_values'].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0		)
       # check size
       self.assertEqual(_lowerCAmelCase							,							(1, 3, 384, 384)		)

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     torch.tensor(
           [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]		).to(_lowerCAmelCase		)
       self.assertTrue(
           torch.allclose(
               outputs.encoder_last_hidden_state[0, 0, :3, :3]							,							_lowerCAmelCase							,							atol=_lowerCAmelCase		)		)

       SCREAMING_SNAKE_CASE_      		=     torch.tensor(
           [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]		).to(_lowerCAmelCase		)
       self.assertTrue(
           torch.allclose(
               outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]							,							_lowerCAmelCase							,							atol=_lowerCAmelCase		)		)

       SCREAMING_SNAKE_CASE_      		=     torch.tensor(
           [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]		).to(_lowerCAmelCase		)
       self.assertTrue(
           torch.allclose(
               outputs.transformer_decoder_last_hidden_state[0, :3, :3]							,							_lowerCAmelCase							,							atol=_lowerCAmelCase		)		)



 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(_lowerCAmelCase		).eval()
       SCREAMING_SNAKE_CASE_      		=     self.default_image_processor
       SCREAMING_SNAKE_CASE_      		=     prepare_img()
       SCREAMING_SNAKE_CASE_      		=     image_processor(_lowerCAmelCase							,							return_tensors='pt'		).to(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     inputs['pixel_values'].shape
       # check size is divisible by 32
       self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0		)
       # check size
       self.assertEqual(_lowerCAmelCase							,							(1, 3, 384, 384)		)

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		)
       # masks_queries_logits
       SCREAMING_SNAKE_CASE_      		=     outputs.masks_queries_logits
       self.assertEqual(
           masks_queries_logits.shape							,							(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)		)
       SCREAMING_SNAKE_CASE_      		=     [
           [-8.7839, -9.0056, -8.8121],
           [-7.4104, -7.0313, -6.5401],
           [-6.6105, -6.3427, -6.4675],
       ]
       SCREAMING_SNAKE_CASE_      		=     torch.tensor(_lowerCAmelCase		).to(_lowerCAmelCase		)
       self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]							,							_lowerCAmelCase							,							atol=_lowerCAmelCase		)		)
       # class_queries_logits
       SCREAMING_SNAKE_CASE_      		=     outputs.class_queries_logits
       self.assertEqual(class_queries_logits.shape							,							(1, model.config.num_queries, model.config.num_labels + 1)		)
       SCREAMING_SNAKE_CASE_      		=     torch.tensor(
           [
               [1.8324, -8.0835, -4.1922],
               [0.8450, -9.0050, -3.6053],
               [0.3045, -7.7293, -3.0275],
           ]		).to(_lowerCAmelCase		)
       self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]							,							_lowerCAmelCase							,							atol=_lowerCAmelCase		)		)




 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_      		=     MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints		).to(_lowerCAmelCase		).eval()
       SCREAMING_SNAKE_CASE_      		=     self.default_image_processor

       SCREAMING_SNAKE_CASE_      		=     image_processor(
           [np.zeros((3, 800, 1_333)		), np.zeros((3, 800, 1_333)		)]							,							segmentation_maps=[np.zeros((384, 384)		).astype(np.floataa		), np.zeros((384, 384)		).astype(np.floataa		)]							,							return_tensors='pt'							,							)

       SCREAMING_SNAKE_CASE_      		=     inputs['pixel_values'].to(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     [el.to(_lowerCAmelCase		) for el in inputs['mask_labels']]
       SCREAMING_SNAKE_CASE_      		=     [el.to(_lowerCAmelCase		) for el in inputs['class_labels']]

       with torch.no_grad():
             SCREAMING_SNAKE_CASE_      		=     model(**_lowerCAmelCase		)

       self.assertTrue(outputs.loss is not None		)

"""simple docstring"""



import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest

from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError

from .utils import require_pil




class 		a_    (			_UpperCAmelCase						):







    def   _snake_case       (      self      :							int  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase        = pa.array(TypedSequence([1, 2, 3]  )  )
        self.assertEqual(arr.type  ,       pa.intaa()  )







    def   _snake_case       (      self      :							List[Any]  )		->Any:






        '''simple docstring'''






        with self.assertRaises(__UpperCamelCase  ):
            _UpperCAmelCase        = pa.array(TypedSequence([1, 2, 3]  )  ,       type=pa.intaa()  )







    def   _snake_case       (      self      :							Tuple  )		->int:






        '''simple docstring'''






        with self.assertRaises(__UpperCamelCase  ):
            _UpperCAmelCase        = pa.array(TypedSequence([1, 2, 3]  ,       try_type=Value("""bool"""  )  ,       type=Value("""int64"""  )  )  )







    def   _snake_case       (      self      :							Optional[Any]  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = pa.array(TypedSequence([1, 2, 3]  ,       type=Value("""int32"""  )  )  )
        self.assertEqual(arr.type  ,       pa.intaa()  )







    def   _snake_case       (      self      :							List[Any]  )		->int:






        '''simple docstring'''






        with self.assertRaises((TypeError, pa.lib.ArrowInvalid)  ):
            _UpperCAmelCase        = pa.array(TypedSequence(["""foo""", """bar"""]  ,       type=Value("""int64"""  )  )  )







    def   _snake_case       (      self      :							int  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = pa.array(TypedSequence([1, 2, 3]  ,       try_type=Value("""int32"""  )  )  )
        self.assertEqual(arr.type  ,       pa.intaa()  )







    def   _snake_case       (      self      :							Dict  )		->str:






        '''simple docstring'''






        _UpperCAmelCase        = pa.array(TypedSequence(["""foo""", """bar"""]  ,       try_type=Value("""int64"""  )  )  )
        self.assertEqual(arr.type  ,       pa.string()  )







    def   _snake_case       (      self      :							Union[str, Any]  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = pa.array(TypedSequence([[[1, 2, 3]]]  ,       type=ArrayaD((1, 3)  ,       """int64"""  )  )  )
        self.assertEqual(arr.type  ,       ArrayaDExtensionType((1, 3)  ,       """int64"""  )  )







    def   _snake_case       (      self      :							Tuple  )		->Any:






        '''simple docstring'''






        with self.assertRaises((TypeError, pa.lib.ArrowInvalid)  ):
            _UpperCAmelCase        = pa.array(TypedSequence(["""foo""", """bar"""]  ,       type=ArrayaD((1, 3)  ,       """int64"""  )  )  )







    def   _snake_case       (      self      :							List[Any]  )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = pa.array(TypedSequence([[[1, 2, 3]]]  ,       try_type=ArrayaD((1, 3)  ,       """int64"""  )  )  )
        self.assertEqual(arr.type  ,       ArrayaDExtensionType((1, 3)  ,       """int64"""  )  )







    def   _snake_case       (      self      :							Any  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = pa.array(TypedSequence(["""foo""", """bar"""]  ,       try_type=ArrayaD((1, 3)  ,       """int64"""  )  )  )
        self.assertEqual(arr.type  ,       pa.string()  )







    @require_pil
    def   _snake_case       (      self      :							Optional[Any]  )		->Tuple:






        '''simple docstring'''






        import PIL.Image

        _UpperCAmelCase        = PIL.Image.fromarray(np.arange(10  ,       dtype=np.uinta  ).reshape(2  ,       5  )  )
        with patch(
            """datasets.arrow_writer.cast_to_python_objects"""  ,       side_effect=__UpperCamelCase  ) as mock_cast_to_python_objects:
            _UpperCAmelCase        = pa.array(TypedSequence([{"""path""": None, """bytes""": b"""image_bytes"""}, pil_image]  ,       type=Image()  )  )
            _UpperCAmelCase     ,_UpperCAmelCase        = mock_cast_to_python_objects.call_args_list[-1]
            self.assertIn("""optimize_list_casting"""  ,       __UpperCamelCase  )
            self.assertFalse(kwargs["""optimize_list_casting"""]  )







def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Dict:





    """simple docstring"""

    _UpperCAmelCase        = pa.BufferReader(_A   ) if isinstance(_A       ,   pa.Buffer   ) else pa.memory_map(_A   )
    _UpperCAmelCase        = pa.ipc.open_stream(_A   )
    _UpperCAmelCase        = f.read_all()
    assert len(pa_table.to_batches()   ) == expected_num_chunks
    assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
    del pa_table


@pytest.mark.parametrize("""writer_batch_size"""       ,   [None, 1, 1_0]   )
@pytest.mark.parametrize(
    """fields"""       ,   [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]   )
def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Any:





    """simple docstring"""

    _UpperCAmelCase        = pa.BufferOutputStream()
    _UpperCAmelCase        = pa.schema(_A   ) if fields else None
    with ArrowWriter(stream=_A       ,   schema=_A       ,   writer_batch_size=_A   ) as writer:
        writer.write({"""col_1""": """foo""", """col_2""": 1}   )
        writer.write({"""col_1""": """bar""", """col_2""": 2}   )
        _UpperCAmelCase     ,_UpperCAmelCase        = writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        _UpperCAmelCase        = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
    assert writer._schema == pa.schema(_A       ,   metadata=writer._schema.metadata   )
    _check_output(output.getvalue()       ,   expected_num_chunks=num_examples if writer_batch_size == 1 else 1   )


def 			_UpperCamelCase						(    )     ->	Dict:





    """simple docstring"""

    _UpperCAmelCase        = pa.BufferOutputStream()
    _UpperCAmelCase        = Features({"""labels""": ClassLabel(names=["""neg""", """pos"""]   )}   )
    with ArrowWriter(stream=_A       ,   features=_A   ) as writer:
        writer.write({"""labels""": 0}   )
        writer.write({"""labels""": 1}   )
        _UpperCAmelCase     ,_UpperCAmelCase        = writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    assert writer._schema == features.arrow_schema
    assert writer._schema.metadata == features.arrow_schema.metadata
    _UpperCAmelCase        = pa.BufferReader(output.getvalue()   )
    _UpperCAmelCase        = pa.ipc.open_stream(_A   )
    _UpperCAmelCase        = f.read_all()
    _UpperCAmelCase        = pa_table.schema
    assert pa_table.num_rows == 2
    assert schema == features.arrow_schema
    assert schema.metadata == features.arrow_schema.metadata
    assert features == Features.from_arrow_schema(_A   )


@pytest.mark.parametrize("""writer_batch_size"""       ,   [None, 1, 1_0]   )
def 			_UpperCamelCase						(    _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = pa.BufferOutputStream()
    with ArrowWriter(
        stream=_A       ,   writer_batch_size=_A       ,   hash_salt="""split_name"""       ,   check_duplicates=_A       ,   ) as writer:
        with pytest.raises(_A   ):
            writer.write({"""col_1""": """foo""", """col_2""": 1}       ,   key=[1, 2]   )
            _UpperCAmelCase     ,_UpperCAmelCase        = writer.finalize()


@pytest.mark.parametrize("""writer_batch_size"""       ,   [None, 2, 1_0]   )
def 			_UpperCamelCase						(    _A   )     ->	List[Any]:





    """simple docstring"""

    _UpperCAmelCase        = pa.BufferOutputStream()
    with ArrowWriter(
        stream=_A       ,   writer_batch_size=_A       ,   hash_salt="""split_name"""       ,   check_duplicates=_A       ,   ) as writer:
        with pytest.raises(_A   ):
            writer.write({"""col_1""": """foo""", """col_2""": 1}       ,   key=1_0   )
            writer.write({"""col_1""": """bar""", """col_2""": 2}       ,   key=1_0   )
            _UpperCAmelCase     ,_UpperCAmelCase        = writer.finalize()


@pytest.mark.parametrize("""writer_batch_size"""       ,   [None, 2, 1_0]   )
def 			_UpperCamelCase						(    _A   )     ->	Dict:





    """simple docstring"""

    _UpperCAmelCase        = pa.BufferOutputStream()
    with ArrowWriter(
        stream=_A       ,   writer_batch_size=_A       ,   hash_salt="""split_name"""       ,   check_duplicates=_A       ,   ) as writer:
        writer.write({"""col_1""": """foo""", """col_2""": 1}       ,   key=1   )
        writer.write({"""col_1""": """bar""", """col_2""": 2}       ,   key=2   )
        _UpperCAmelCase     ,_UpperCAmelCase        = writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    _check_output(output.getvalue()       ,   expected_num_chunks=num_examples if writer_batch_size == 1 else 1   )


@pytest.mark.parametrize("""writer_batch_size"""       ,   [None, 1, 1_0]   )
@pytest.mark.parametrize(
    """fields"""       ,   [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]   )
def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Any:





    """simple docstring"""

    _UpperCAmelCase        = pa.BufferOutputStream()
    _UpperCAmelCase        = pa.schema(_A   ) if fields else None
    with ArrowWriter(stream=_A       ,   schema=_A       ,   writer_batch_size=_A   ) as writer:
        writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}   )
        writer.write_batch({"""col_1""": [], """col_2""": []}   )
        _UpperCAmelCase     ,_UpperCAmelCase        = writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        _UpperCAmelCase        = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
    assert writer._schema == pa.schema(_A       ,   metadata=writer._schema.metadata   )
    _check_output(output.getvalue()       ,   expected_num_chunks=num_examples if writer_batch_size == 1 else 1   )


@pytest.mark.parametrize("""writer_batch_size"""       ,   [None, 1, 1_0]   )
@pytest.mark.parametrize(
    """fields"""       ,   [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]   )
def 			_UpperCamelCase						(    _A       ,   _A   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = pa.BufferOutputStream()
    _UpperCAmelCase        = pa.schema(_A   ) if fields else None
    with ArrowWriter(stream=_A       ,   schema=_A       ,   writer_batch_size=_A   ) as writer:
        writer.write_table(pa.Table.from_pydict({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}   )   )
        _UpperCAmelCase     ,_UpperCAmelCase        = writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        _UpperCAmelCase        = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
    assert writer._schema == pa.schema(_A       ,   metadata=writer._schema.metadata   )
    _check_output(output.getvalue()       ,   expected_num_chunks=num_examples if writer_batch_size == 1 else 1   )


@pytest.mark.parametrize("""writer_batch_size"""       ,   [None, 1, 1_0]   )
@pytest.mark.parametrize(
    """fields"""       ,   [None, {"""col_1""": pa.string(), """col_2""": pa.intaa()}, {"""col_1""": pa.string(), """col_2""": pa.intaa()}]   )
def 			_UpperCamelCase						(    _A       ,   _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = pa.BufferOutputStream()
    _UpperCAmelCase        = pa.schema(_A   ) if fields else None
    with ArrowWriter(stream=_A       ,   schema=_A       ,   writer_batch_size=_A   ) as writer:
        writer.write_row(pa.Table.from_pydict({"""col_1""": ["""foo"""], """col_2""": [1]}   )   )
        writer.write_row(pa.Table.from_pydict({"""col_1""": ["""bar"""], """col_2""": [2]}   )   )
        _UpperCAmelCase     ,_UpperCAmelCase        = writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    if not fields:
        _UpperCAmelCase        = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
    assert writer._schema == pa.schema(_A       ,   metadata=writer._schema.metadata   )
    _check_output(output.getvalue()       ,   expected_num_chunks=num_examples if writer_batch_size == 1 else 1   )


def 			_UpperCamelCase						(    )     ->	Dict:





    """simple docstring"""

    with tempfile.TemporaryDirectory() as tmp_dir:
        _UpperCAmelCase        = {"""col_1""": pa.string(), """col_2""": pa.intaa()}
        _UpperCAmelCase        = os.path.join(_A       ,   """test.arrow"""   )
        with ArrowWriter(path=_A       ,   schema=pa.schema(_A   )   ) as writer:
            writer.write_batch({"""col_1""": ["""foo""", """bar"""], """col_2""": [1, 2]}   )
            _UpperCAmelCase     ,_UpperCAmelCase        = writer.finalize()
        assert num_examples == 2
        assert num_bytes > 0
        assert writer._schema == pa.schema(_A       ,   metadata=writer._schema.metadata   )
        _check_output(_A       ,   1   )


def 			_UpperCamelCase						(    _A   )     ->	Optional[int]:





    """simple docstring"""

    if pa.types.is_list(_A   ):
        return get_base_dtype(arr_type.value_type   )
    else:
        return arr_type


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	str:





    """simple docstring"""

    if isinstance(lst[0]       ,   _A   ):
        change_first_primitive_element_in_list(lst[0]       ,   _A   )
    else:
        _UpperCAmelCase        = value


@pytest.mark.parametrize("""optimized_int_type, expected_dtype"""       ,   [(None, pa.intaa()), (Value("""int32"""   ), pa.intaa())]   )
@pytest.mark.parametrize("""sequence"""       ,   [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]   )
def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	Optional[int]:





    """simple docstring"""

    _UpperCAmelCase        = pa.array(TypedSequence(_A       ,   optimized_int_type=_A   )   )
    assert get_base_dtype(arr.type   ) == expected_dtype


@pytest.mark.parametrize(
    """col, expected_dtype"""       ,   [
        ("""attention_mask""", pa.inta()),
        ("""special_tokens_mask""", pa.inta()),
        ("""token_type_ids""", pa.inta()),
        ("""input_ids""", pa.intaa()),
        ("""other""", pa.intaa()),
    ]       ,   )
@pytest.mark.parametrize("""sequence"""       ,   [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]]   )
def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	Any:





    """simple docstring"""

    _UpperCAmelCase        = pa.array(OptimizedTypedSequence(_A       ,   col=_A   )   )
    assert get_base_dtype(arr.type   ) == expected_dtype

    # not in range
    if col != "other":
        # avoids errors due to in-place modifications
        _UpperCAmelCase        = copy.deepcopy(_A   )
        _UpperCAmelCase        = np.iinfo(expected_dtype.to_pandas_dtype()   ).max + 1
        change_first_primitive_element_in_list(_A       ,   _A   )
        _UpperCAmelCase        = pa.array(OptimizedTypedSequence(_A       ,   col=_A   )   )
        assert get_base_dtype(arr.type   ) == pa.intaa()


@pytest.mark.parametrize("""raise_exception"""       ,   [False, True]   )
def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase        = str(tmp_path / """dataset-train.arrow"""   )
    try:
        with ArrowWriter(path=_A   ) as writer:
            if raise_exception:
                raise pa.lib.ArrowInvalid()
            else:
                writer.stream.close()
    except pa.lib.ArrowInvalid:
        pass
    finally:
        assert writer.stream.closed


def 			_UpperCamelCase						(    _A   )     ->	List[str]:





    """simple docstring"""

    _UpperCAmelCase        = """mock://dataset-train.arrow"""
    with ArrowWriter(path=_A       ,   storage_options=mockfs.storage_options   ) as writer:
        assert isinstance(writer._fs       ,   type(_A   )   )
        assert writer._fs.storage_options == mockfs.storage_options
        writer.write({"""col_1""": """foo""", """col_2""": 1}   )
        writer.write({"""col_1""": """bar""", """col_2""": 2}   )
        _UpperCAmelCase     ,_UpperCAmelCase        = writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    assert mockfs.exists(_A   )


def 			_UpperCamelCase						(    )     ->	List[Any]:





    """simple docstring"""

    _UpperCAmelCase        = pa.BufferOutputStream()
    with ParquetWriter(stream=_A   ) as writer:
        writer.write({"""col_1""": """foo""", """col_2""": 1}   )
        writer.write({"""col_1""": """bar""", """col_2""": 2}   )
        _UpperCAmelCase     ,_UpperCAmelCase        = writer.finalize()
    assert num_examples == 2
    assert num_bytes > 0
    _UpperCAmelCase        = pa.BufferReader(output.getvalue()   )
    _UpperCAmelCase        = pq.read_table(_A   )
    assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}


@require_pil
@pytest.mark.parametrize("""embed_local_files"""       ,   [False, True]   )
def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Union[str, Any]:





    """simple docstring"""

    import PIL.Image

    _UpperCAmelCase        = str(tmp_path / """test_image_rgb.jpg"""   )
    PIL.Image.fromarray(np.zeros((5, 5)       ,   dtype=np.uinta   )   ).save(_A       ,   format="""png"""   )
    _UpperCAmelCase        = pa.BufferOutputStream()
    with ParquetWriter(
        stream=_A       ,   features=Features({"""image""": Image()}   )       ,   embed_local_files=_A   ) as writer:
        writer.write({"""image""": image_path}   )
        writer.finalize()
    _UpperCAmelCase        = pa.BufferReader(output.getvalue()   )
    _UpperCAmelCase        = pq.read_table(_A   )
    _UpperCAmelCase        = pa_table.to_pydict()
    if embed_local_files:
        assert isinstance(out["""image"""][0]["""path"""]       ,   _A   )
        with open(_A       ,   """rb"""   ) as f:
            assert out["image"][0]["bytes"] == f.read()
    else:
        assert out["image"][0]["path"] == image_path
        assert out["image"][0]["bytes"] is None







def 			_UpperCamelCase						(    )     ->	Tuple:





    """simple docstring"""

    _UpperCAmelCase        = pa.schema([pa.field("""col_1"""       ,   pa.string()       ,   nullable=_A   )]   )
    _UpperCAmelCase        = pa.BufferOutputStream()
    with ArrowWriter(stream=_A   ) as writer:
        writer._build_writer(inferred_schema=_A   )
        assert writer._schema == pa.schema([pa.field("""col_1"""       ,   pa.string()   )]   )